885 citations · 2.4k across the 41 of their papers we have counts for
7 papers · 1 filter
ElementaryNet: A Non-Strategic Neural Network for Predicting Human Behavior in Normal-Form Games
Greg d'Eon, Hala Murad, Kevin Leyton-Brown +1
Behavioral game theory models serve two purposes: yielding insights into how human decision-making works, and predicting how people would behave in novel strategic settings. A syst…
How to Evaluate Behavioral Models
Greg d'Eon, Sophie Greenwood, Kevin Leyton-Brown +1
Researchers building behavioral models, such as behavioral game theorists, use experimental data to evaluate predictive models of human behavior. However, there is little agreement…
The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models
Greg d'Eon, Jason d'Eon, James R. Wright +1
Supervised learning models often make systematic errors on rare subsets of the data. When these subsets correspond to explicit labels in the data (e.g., gender, race) such poor per…
The Perils of Learning Before Optimizing
Chris Cameron, Jason Hartford, Taylor Lundy +1
Formulating real-world optimization problems often begins with making predictions from historical data (e.g., an optimizer that aims to recommend fast routes relies upon travel-tim…
Exemplar Guided Active Learning
Jason Hartford, Kevin Leyton-Brown, Hadas Raviv +3
We consider the problem of wisely using a limited budget to label a small subset of a large unlabeled dataset. We are motivated by the NLP problem of word sense disambiguation. For…
PMI-Masking: Principled masking of correlated spans
Yoav Levine, Barak Lenz, Opher Lieber +4
Masking tokens uniformly at random constitutes a common flaw in the pretraining of Masked Language Models (MLMs) such as BERT. We show that such uniform masking allows an MLM to mi…